# -*- coding: utf-8 -*-
from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.ods.mysql.pwo_processing_record import jms_ods__pwo_processing_record

jms_dwd__dwd_pwo_processing_record_base_dt = SparkSqlOperator(
    task_id='jms_dwd__dwd_pwo_processing_record_base_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    execution_timeout=timedelta(hours=1),
    email=['payne.jiang@jtexpress.com', 'yl_bigdata@yl-scm.com'],
    name='jms_dwd__dwd_pwo_processing_record_base_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/dwd_pwo_processing_record_base_dt/execute.sql',
    driver_memory='5G',
    driver_cores=2,
    executor_cores=2,
    executor_memory='4G',
    num_executors=4,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          #'spark.dynamicAllocation.maxExecutors': 100,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.maxExecutors': 11,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 180,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '2G',  # 堆外内存
          'spark.sql.shuffle.partitions': 200,
          'spark.default.paralleism': 200,
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          'spark.network.timeout': 900,
          'spark.core.connection.ack.wait.timeout': 300,
          'spark.sql.autoBroadcastJoinThreshold': 104857600,
          },

    yarn_queue='pro',
)
jms_dwd__dwd_pwo_processing_record_base_dt<< [
    jms_ods__pwo_processing_record
]
